outlierSamples: detects outlier samples in qPCR data

In MCMC.qpcr: Bayesian Analysis of qRT-PCR Data

Description

reports samples that have too little starting material relative to others (by default, less by two standard deviations)

Usage

outlierSamples(model, data, z.cutoff = -2)

Arguments

model	qPCR model: the output of mcmc.qpcr or mcmc.qpcr.lognormal function fitted with pr=TRUE option
data	The dataset that was analysed to generate the model (output of cq2counts or cq2log functions)
z.cutoff	z-score cutoff to report an outlier sample.

Value

A vector containing outlier sample names.

Author(s)

Mikhail V. Matz, University of Texas at Austin <[e-mail]>

References

Matz MV, Wright RM, Scott JG (2013) No Control Genes Required: Bayesian Analysis of qRT-PCR Data. PLoS ONE 8(8): e71448. doi:10.1371/journal.pone.0071448

Examples

# loading Cq data and amplification efficiencies
data(coral.stress) 
data(amp.eff) 
# extracting a subset of data 
cs.short=subset(coral.stress, timepoint=="one")

genecolumns=c(5,6,16,17) # specifying columns corresponding to genes of interest
conditions=c(1:4) # specifying columns containing factors  

# calculating molecule counts and reformatting:
dd=cq2counts(data=cs.short,genecols=genecolumns,
condcols=conditions,effic=amp.eff,Cq1=37) 

# fitting the model
mm=mcmc.qpcr(
	fixed="condition",
	data=dd,
	controls=c("nd5","rpl11"),
	nitt=4000, # remove this line when analyzing real data!
	pr=TRUE
)

# detecting outliers
outliers=outlierSamples(mm,dd)

# removing outliers
dd=dd[!(dd$sample %in% outliers),]

MCMC.qpcr documentation built on March 31, 2020, 5:22 p.m.